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Record W2183215930

Towards a Road Map for Mitigating the Rates and Occurrences of Long-Term Wellbore Leakage

2014· article· en· W2183215930 on OpenAlexaboutno aff
Maurice B. Dusseault, Richard E. Jackson, Daniel J. MacDonald

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCasingWellboreLeakage (economics)Petroleum engineeringEnvironmental scienceEngineeringMining engineeringForensic engineeringRisk analysis (engineering)
DOInot available

Abstract

fetched live from OpenAlex

Wellbore leakage, the seepage of natural gas through cement channels, casing annuli and behind the outermost casing string, is a problem reported across Canada. Wellbore leakage is a threat to the environment and public safety because of potential groundwater quality deterioration, contributions to greenhouse gas emissions and explosion risks if methane gas accumulates in inadequately ventilated areas. Leakage rates remain poorly quantified and remedial workovers are often challenging. Subsequent costs attributed to remedial workovers are often significant and present an economic strain on the industry as well as lost profit, reduced exploration and production and, therefore, foregone royalties. The purpose of this report has been to (1) identify persistent problems that result in wellbore leakage, (2) discuss potential approaches that appear to reduce the rates and occurrences of wellbore leakage, (3) describe methods for detecting and monitoring for wellbore leakage, and (4) discuss methods that have improved the efficiency of remedial workovers. Our motivation has been to outline the need for a Canadian Road Map for Wellbore Integrity that identifies future research and development (R&D) needs and identifies where the resources for such R&D might be found. Several key processes were identified that lead to the potential development of a leakage problem, working to either prevent the initial creation of an adequate cement seal or compromising the integrity of the cement sheath over time. The pathways produced by these processes include microannuli, channels and fractures due to poor mud removal, invasion by fluids during setting, stresses imposed by operations, cement shrinkage and casing corrosion. Intermediate-depth formations, i.e., non-commercial gas zones, are often found to be the source of the buoyant fugitive gases that migrate up these pathways. ‘Doing it right the first time’ – i.e., creating a robust seal during primary cementation – was uniformly agreed by industry and regulators to be the best approach for reducing leakage development over the operational and post-operational lifetime of a well. Even if an adequately sealed wellbore was achieved during primary cementation, there remains the possibility that a leakage problem may develop due to corrosion or cement shrinkage. Therefore, in addition to ‘doing it right the first time’, new cement formulations, wellbore designs and abandonment approaches are needed. Wellbore monitoring needs to be improved by adopting the use of newer technologies and undergoing more thorough subsurface monitoring. Remedial workovers require advances in source identification (such as enhanced acoustic logging technology and isotopic fingerprinting) and alternative-sealing materials. Wellbore leakage will likely only become worse with time as new wells are completed and old wells are abandoned. We recommend that a Canadian working group be established to develop a Road Map for Wellbore Integrity R&D to improve long-term wellbore integrity. Hydraulic fracturing is perceived as a threat by many in the public, however, we believe that this concern is misplaced. Because of the real issues associated with greenhouse gas emissions and possible groundwater quality deterioration, we believe the more significant issue affecting the social license of the oil and gas industry is long-term wellbore integrity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.229
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2014
Admission routes1
Has abstractyes

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